Search query intent

ABSTRACT

A method and system for improving a search query process is provided. The method includes analyzing via a natural language classifier (NLC) circuit of a hardware device, a partial search phase entered in a search field of a graphical user interface with respect to a search query for specified subject matter. A subject based intent classification associated with the search query is determined and compared to intent based data of an intent data repository. In response, an autocomplete phrase associated with the subject based intent classification and the partial search phrase is generated and presented to a user via the graphical user interface.

FIELD

The present invention relates generally to a method implementing asearch query and in particular to a method and associated system forimproving computer search query technology by determining intentassociated with a search query.

BACKGROUND

Accurately predicting search parameters based on partial input from auser typically includes an inaccurate process with little flexibility.Analyzing current search parameters with respect past search parametersmay include a complicated process that may be time consuming and requirea large amount of resources. Accordingly, there exists a need in the artto overcome at least some of the deficiencies and limitations describedherein above.

SUMMARY

A first aspect of the invention provides a search query improvementmethod comprising: analyzing, by a processor enabling a natural languageclassifier (NLC) circuit, a partial search phase entered in a searchfield of a graphical user interface (GUI) with respect to a search queryfor specified subject matter; determining, by the processor executingthe NLC circuit with respect to results of the analyzing, a subjectbased intent classification associated with the search query; comparing,by the processor, the subject based intent classification to intentbased data of an intent data repository; automatically generating, bythe processor based on results of the comparing, an autocomplete phraseassociated with the subject based intent classification and the partialsearch phrase; and presenting, by the processor to a user via the GUI,the autocomplete phrase.

A second aspect of the invention provides a computer program product,comprising a computer readable hardware storage device storing acomputer readable program code, the computer readable program codecomprising an algorithm that when executed by a processor of a hardwaredevice implements a search query improvement method, the methodcomprising: analyzing, by the processor enabling a natural languageclassifier (NLC) circuit of the hardware device, a partial search phaseentered in a search field of a graphical user interface (GUI) withrespect to a search query for specified subject matter; determining, bythe processor executing the NLC circuit with respect to results of theanalyzing, a subject based intent classification associated with thesearch query; comparing, by the processor, the subject based intentclassification to intent based data of an intent data repository;automatically generating, by the processor based on results of thecomparing, an autocomplete phrase associated with the subject basedintent classification and the partial search phrase; and presenting, bythe processor to a user via the GUI, the autocomplete phrase.

A third aspect of the invention provides a hardware device comprising aprocessor coupled to a computer-readable memory unit, the memory unitcomprising instructions that when executed by the processor executes asearch query improvement method comprising: analyzing, by the processorenabling a natural language classifier (NLC) circuit of the hardwaredevice, a partial search phase entered in a search field of a graphicaluser interface (GUI) with respect to a search query for specifiedsubject matter; determining, by the processor executing the NLC circuitwith respect to results of the analyzing, a subject based intentclassification associated with the search query; comparing, by theprocessor, the subject based intent classification to intent based dataof an intent data repository; automatically generating, by the processorbased on results of the comparing, an autocomplete phrase associatedwith the subject based intent classification and the partial searchphrase; and presenting, by the processor to a user via the GUI, theautocomplete phrase.

The present invention advantageously provides a simple method andassociated system capable of predicting search parameters based onpartial input from a user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for improving computer search querytechnology by determining intent associated with a search query, inaccordance with embodiments of the present invention.

FIG. 2 illustrates an algorithm detailing a process flow enabled by thesystem of FIG. 1 for improving computer search query technology bydetermining intent associated with a search query, in accordance withembodiments of the present invention.

FIG. 3A illustrates a screen shot of a user interface enabled by thesystem of FIG. 1 for improving computer search query technology bydetermining intent associated with a search query, in accordance withembodiments of the present invention.

FIG. 3B illustrates a screen shot of a user interface enabled by thesystem of FIG. 1 for presenting an intent based confidence score, inaccordance with embodiments of the present invention.

FIG. 4 illustrates a computer system used by the system of FIG. 1 forenabling a process for improving computer search query technology bydetermining intent associated with a search query, in accordance withembodiments of the present invention.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 100 for improving computer search querytechnology by determining intent associated with a search query, inaccordance with embodiments of the present invention. System 100 enablesa process for auto-completing words and/or search queries by identifyinga type of intent associated with a user search query. Intent is definedherein as an aim or purpose with respect to a subject associated with auser search query. The auto complete process is executed by analyzingindicators associated with an intent of a search query and predicting acomplete word or phrase/result for the search query before a queryphrase is fully entered (by a user) into a search field of a graphicaluser interface (GUI). The analysis includes enabling a natural languageclassifier (NLC) circuit 19 to analyze a user partial user query inputand cognitively matching all possible detected patterns associated withthe user query input with data from an autocomplete result repository(e.g., intent data repository 29) to identify the intent of the userquery. NLC circuit 19 applies deep learning techniques for predicting“best” predefined classes or categories associated with short inputsentences or phrases. The classes or categories may trigger acorresponding action with respect to an application such as, inter alia,directing a request to a location or person, answering a question, etc.After the deep learning techniques have completed execution, NLC circuit19 returns information associated with unknown text and a response mayinclude the name for top classes and confidence values.

System 100 of FIG. 1 includes hardware devices 114 a . . . 114 n and anintent data repository 29 in communication with a hardware apparatus 14via a network 118. Hardware devices 114 a . . . 114 n and hardwareapparatus 14 each may comprise an embedded computer. An embeddedcomputer is defined herein as a remotely portable dedicated computercomprising a combination of computer hardware and software (fixed incapability or programmable) specifically designed for executing aspecialized function. Programmable embedded computers may comprisespecialized programming interfaces. Additionally, hardware devices 114 a. . . 114 n and hardware apparatus 14 may each comprise a specializedhardware device comprising specialized (non-generic) hardware andcircuitry (i.e., specialized discrete non-generic analog, digital, andlogic based circuitry) for executing a process described with respect toFIGS. 1-3. The specialized discrete non-generic analog, digital, andlogic based circuitry may include proprietary specially designedcomponents (e.g., a specialized integrated circuit such as a naturallanguage classifier (NLC) circuit 19 and auto complete circuit 23 (asdescribed, infra) designed for only implementing an automated processfor determining an intent associated with a search query). Hardwareapparatus 14 includes a memory system 8, software 17, NLC circuit 19,and an auto complete circuit 23. The memory system 8 (e.g., a database)and intent data repository 29 may each include a single memory system.Alternatively, the memory system 8 and intent data repository 29 mayeach include a plurality of memory systems. Hardware devices 114 a . . .114 n may comprise any type of hardware devices (comprising embeddedcircuitry for only performing an automated process for determining anintent associated with a search query) including, inter alia, a smartphone, a PDA, a tablet computer, a laptop computer, etc.

System 100 of FIG. 1 enables a process for determining intent associatedwith a search query as follows:

During a process for enabling a natural language search (initiated by auser) at a Website, an application programming interface (API) forderiving an intent of the user (e.g., via natural language classifiers(NLC)) is applied with respect to a partially completed natural languagesearch phrase entered in a search application GUI. For example, theintent may be determined with respect to the partially completed naturallanguage search phrase being directed toward products, support, orcontent classifications, as defined within a ground truth (i.e.,information provided by direct observation) for the Website. If anintent is not determined via the natural language search, a standard setof relevant options (i.e., with respect to past searches) retrieved froman autocomplete result repository may be returned via an autocompleteresult generator circuit. If an intent is determined via the naturallanguage search, a tailored auto completion result is generated by theautocomplete result generator circuit 23 based on an intentclassification(s) and a standard set of results from the autocompleteresult repository comprising results from previous search queries. Thetailored auto completion result is subsequently presented to the uservia an autocomplete selection mechanism (e.g., a specialized circuit andGUI). For example, if a user wishes to locate help with respect torepairing a bicycle tire and begins to type the phrase “How do I fix abike”, a natural language classification process is continuouslyexecuted with respect to the partial search query resulting in an intentof “support” being determined to be closely correlated within a groundtruth for the Website. The intent of “support” is determined because thenatural language classifier's cognitive matching capabilities resultedin completed matches (with respect to a high confidence value) withrespect to all possible detected patterns in the natural language searchquery. Examples of the detected patterns may include, inter alia, thefollowing phrases: “how do I”, “I fix”, “a bike”, etc. such that alllend of the aforementioned detected patterns provide evidence withrespect to the detected intent of “support”. Furthermore, manyadditional intent phrases such as “cycling” or “consumables” could havebeen detected based on the detected patterns. A standard set of resultsmay be generated by the auto complete circuit 23 if no viable intent(s)is detected via execution of the natural language search query therebyyielding a standard set of auto complete results. As more intent phrasesare inferred from the partial query, the auto complete circuit 23receives additional context associated with a subset of results morerelevant to the user. Therefore, the auto complete circuit 23 refinesthe results (with respect to intent) based on the specific intent(s) ofthe user thereby yielding a tailored list of auto-completion results forthe partially entered search query provided to the user. For example (inthis instance), a selection entitled “How do I patch a bicycle tire?”enables system 100 to respond with alternative word or phrases(differing from originally entered text) such that an actual intentphrase is determined.

FIG. 2 illustrates an algorithm detailing a process flow enabled bysystem 100 of FIG. 1 for improving computer search query technology bydetermining intent associated with a search query, in accordance withembodiments of the present invention. Each of the steps in the algorithmof FIG. 2 may be enabled and executed in any order by a computerprocessor(s) or any type of specialized hardware executing specializedcomputer code. In step 200, a partial search phase entered in a searchfield of a GUI (with respect to a search query (associated with aspecified Website) for specified subject matter) is analyzed by a NLCcircuit. In step 202, a subject based intent classification associatedwith the search query is determined based on the analysis of step 200.In step 204, the subject based intent classification is compared tointent based data of an intent data repository. In step 210, allpossible patterns associated with the partial search phase are detected.In step 212, an autocomplete phrase associated with the subject basedintent classification and the partial search phrase is automaticallygenerating based on results of steps 204 and 210. In step 214, theautocomplete phrase is presented to a user via the GUI. Additionally, aconfidence percentage value may be presented with the autocompletephrase. The confidence percentage value is associated with a confidencefactor with respect to the intent classification. In step 216, theautocomplete phrase is stored within the intent data repository forfuture use.

FIG. 3A illustrates a screen shot of a user interface 300 a enabled bysystem 100 of FIG. 1 for improving computer search query technology bydetermining intent associated with a search query, in accordance withembodiments of the present invention. Additionally, user interface 300 aenables a process for presenting autocomplete phrases associated with apartial search query. The autocomplete phrases illustrate a similarintent with respect to the partial search query without the need forexact word suggestions. User interface 300 a comprises an input field302 a comprising an inputted search query for the phrase “how to fix aflat”. In response, system 100 presents results comprising intent basedphrases 307 comprising: a phrase 307 a that includes all of the inputtedsearch query phrase with the addition of the phrase “bicycle tire” and aphrase 307 b that includes some of the inputted search query phrase “howto” with the addition of the phrase “remove a bike tube”.

FIG. 3B illustrates a screen shot of a user interface 300 b enabled bysystem 100 of FIG. 1 for improving computer search query technology bydetermining intent associated with a search query, in accordance withembodiments of the present invention. User interface 300 b comprises aninput field 302 a comprising an inputted search query for the phrase“how to fix a flat”. In response, system 100 presents results comprisingintent based phrases 309 with an associated intent based confidencescore of 95%.

FIG. 4 illustrates a computer system 90 (e.g., hardware devices 114 a .. . 114 n and hardware apparatus 14) used by or comprised by the systemof FIG. 1 for improving computer search query technology by determiningintent associated with a search query, in accordance with embodiments ofthe present invention.

Aspects of the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, microcode, etc.) or an embodiment combiningsoftware and hardware aspects that may all generally be referred toherein as a “circuit,” “module,” or “system.”

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing apparatus receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, device(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing device to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing device, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing device, and/or other devicesto function in a particular manner, such that the computer readablestorage medium having instructions stored therein comprises an articleof manufacture including instructions which implement aspects of thefunction/act specified in the flowchart and/or block diagram block orblocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing device, or other device tocause a series of operational steps to be performed on the computer,other programmable device or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable device, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The computer system 90 illustrated in FIG. 4 includes a processor 91, aninput device 92 coupled to the processor 91, an output device 93 coupledto the processor 91, and memory devices 94 and 95 each coupled to theprocessor 91. The input device 92 may be, inter alia, a keyboard, amouse, a camera, a touchscreen, etc. The output device 93 may be, interalia, a printer, a plotter, a computer screen, a magnetic tape, aremovable hard disk, a floppy disk, etc. The memory devices 94 and 95may be, inter alia, a hard disk, a floppy disk, a magnetic tape, anoptical storage such as a compact disc (CD) or a digital video disc(DVD), a dynamic random access memory (DRAM), a read-only memory (ROM),etc. The memory device 95 includes a computer code 97. The computer code97 includes algorithms (e.g., the algorithm of FIG. 2) for enabling aprocess for improving computer search query technology by determiningintent associated with a search query. The processor 91 executes thecomputer code 97. The memory device 94 includes input data 96. The inputdata 96 includes input required by the computer code 97. The outputdevice 93 displays output from the computer code 97. Either or bothmemory devices 94 and 95 (or one or more additional memory devices suchas read only memory device 96) may include algorithms (e.g., thealgorithm of FIG. 2) and may be used as a computer usable medium (or acomputer readable medium or a program storage device) having a computerreadable program code embodied therein and/or having other data storedtherein, wherein the computer readable program code includes thecomputer code 97. Generally, a computer program product (or,alternatively, an article of manufacture) of the computer system 90 mayinclude the computer usable medium (or the program storage device).

In some embodiments, rather than being stored and accessed from a harddrive, optical disc or other writeable, rewriteable, or removablehardware memory device 95, stored computer program code 84 (e.g.,including the algorithm of FIG. 2) may be stored on a static,nonremovable, read-only storage medium such as a Read-Only Memory (ROM)device 85, or may be accessed by processor 91 directly from such astatic, nonremovable, read-only medium 85. Similarly, in someembodiments, stored computer program code 97 may be stored ascomputer-readable firmware 85, or may be accessed by processor 91directly from such firmware 85, rather than from a more dynamic orremovable hardware data-storage device 95, such as a hard drive oroptical disc.

Still yet, any of the components of the present invention could becreated, integrated, hosted, maintained, deployed, managed, serviced,etc. by a service supplier who offers to enable a process for improvingcomputer search query technology by determining intent associated with asearch query. Thus, the present invention discloses a process fordeploying, creating, integrating, hosting, maintaining, and/orintegrating computing infrastructure, including integratingcomputer-readable code into the computer system 90, wherein the code incombination with the computer system 90 is capable of performing amethod for enabling a process for improving computer search querytechnology by determining intent associated with a search query. Inanother embodiment, the invention provides a business method thatperforms the process steps of the invention on a subscription,advertising, and/or fee basis. That is, a service supplier, such as aSolution Integrator, could offer to enable a process for improvingcomputer search query technology by determining intent associated with asearch query. In this case, the service supplier can create, maintain,support, etc. a computer infrastructure that performs the process stepsof the invention for one or more customers. In return, the servicesupplier can receive payment from the customer(s) under a subscriptionand/or fee agreement and/or the service supplier can receive paymentfrom the sale of advertising content to one or more third parties.

While FIG. 4 shows the computer system 90 as a particular configurationof hardware and software, any configuration of hardware and software, aswould be known to a person of ordinary skill in the art, may be utilizedfor the purposes stated supra in conjunction with the particularcomputer system 90 of FIG. 4. For example, the memory devices 94 and 95may be portions of a single memory device rather than separate memorydevices.

While embodiments of the present invention have been described hereinfor purposes of illustration, many modifications and changes will becomeapparent to those skilled in the art. Accordingly, the appended claimsare intended to encompass all such modifications and changes as fallwithin the true spirit and scope of this invention.

What is claimed is:
 1. A Website search query improvement methodcomprising: analyzing, by a processor enabling a natural languageclassifier (NLC) circuit of an embedded hardware device, a partialsearch phrase entered in a search field of a graphical user interface(GUI) with respect to a search query for specified subject matter;continuously detecting, by the NLC circuit via execution of an automatednatural language search query, all possible patterns of the partialsearch phrase; matching, by the NLC circuit, all of the detectedpossible patterns with respect to information within an autocompleteresult repository database; executing, by the NLC circuit, a deeplearning process for automated prediction of classes and categories forthe partial search phrase; triggering, by the NLC circuit, an actionwith respect to software application execution; determining viaapplication of an application programming interface (API), by theprocessor executing the NLC circuit with respect to results of theanalyzing, a subject based intent classification associated with thesearch query for content classifications defined within a ground truthfor the Website; determining, by the NLC circuit, that said subjectbased intent classification comprises a confidence factor of less than100 percent confidence with respect to the subject based intentclassification being correct; comparing, by the processor, the subjectbased intent classification to intent based data of an intent datarepository; generating, by said processor based on results of saiddetermining that said subject based intent classification comprises aconfidence factor of less than 100 percent confidence and saidcomparing, a subset of search results of search results data, whereinsaid subset of search results correlates to said subject based intentclassification; automatically generating, by the processor based onresults of the comparing, the matching, the executing, the triggering,the generating, and the detecting, an autocomplete phrase associatedwith the subject based intent classification and the partial searchphrase; presenting, by said processor to a user via an autocompleteselection mechanism comprising a specialized circuit and the GUI, theautocomplete phrase in combination with additional autocomplete phrasesand a single percentage value of said confidence factor, wherein saidsingle percentage value comprises a single composite value indicating atop intent subject classification value associated with saidautocomplete phrase in combination with additional autocomplete phrases,and wherein said single percentage value of said confidence factor ispresented in a specified arrangement, via said GUI, adjacent to and inbetween said partial search phrase entered in said search field and saidautocomplete phrase in combination with additional autocomplete phrases;executing, by said processor, an improved Web based search with respectto said autocomplete phrase; directing, by said processor based onresults of said executing, a Web application to a specified Web locationassociated with said autocomplete phrase and said Website; presenting,by said processor based on results of said directing, classes ofinformation associated with said autocomplete phrase, wherein saidclasses of information are configured to enable said triggering;receiving, by said processor, additional context associated with anadditional subset of results determined to be more relevant to saiduser; and refining, by said processor executing an auto complete circuitof said embedded hardware device, said additional subset of resultsbased on a specific determined intent of the user thereby yielding atailored list of auto-completion results for said partial search phrase.2. The method of claim 1, wherein the intent based data comprises dataretrieved during previous search queries associated with the subjectbased intent classification.
 3. The method of claim 1, furthercomprising: transmitting, by the processor to the intent datarepository, said autocomplete phrase, wherein the autocomplete phrase isstored in the intent data repository.
 4. The method of claim 1, whereinthe query is associated with a specified Website network.
 5. The methodof claim 1, wherein the presenting the autocomplete phrase comprisespresenting a confidence percentage value with the autocomplete phrase,and wherein the confidence percentage value is associated with aconfidence factor with respect to the intent classification.
 6. Themethod of claim 1, wherein the autocomplete phrase comprises a portionof the partial search phrase.
 7. The method of claim 1, furthercomprising: providing at least one support service for at least one ofcreating, integrating, hosting, maintaining, and deployingcomputer-readable code in the hardware device, the code being executedby the computer processor to implement: the determining, the comparing,the automatically generating, and the presenting.
 8. A computer programproduct, comprising a computer readable hardware storage device storinga computer readable program code, said computer readable program codecomprising an algorithm that when executed by a processor of a hardwaredevice implements a Website search query improvement method, the methodcomprising: analyzing, by the processor enabling a natural languageclassifier (NLC) circuit of an embedded hardware device, a partialsearch phrase entered in a search field of a graphical user interface(GUI) with respect to a search query for specified subject matter;continuously detecting, by the NLC circuit via execution of an automatednatural language search query, all possible patterns of the partialsearch phrase; matching, by the NLC circuit, all of the detectedpossible patterns with respect to information within an autocompleteresult repository database; executing, by the NLC circuit, a deeplearning process for automated prediction of classes and categories forthe partial search phrase; triggering, by the NLC circuit, an actionwith respect to software application execution; determining viaapplication of an application programming interface (API), by theprocessor executing the NLC circuit with respect to results of theanalyzing, a subject based intent classification associated with thesearch query for content classifications defined within a ground truthfor the Website; determining, by the NLC circuit, that said subjectbased intent classification comprises a confidence factor of less than100 percent confidence with respect to the subject based intentclassification being correct; comparing, by the processor, the subjectbased intent classification to intent based data of an intent datarepository; generating, by said processor based on results of saiddetermining that said subject based intent classification comprises aconfidence factor of less than 100 percent confidence and saidcomparing, a subset of search results of search results data, whereinsaid subset of search results correlates to said subject based intentclassification; automatically generating, by the processor based onresults of the comparing, the matching, the executing, the triggering,the generating, and the detecting, an autocomplete phrase associatedwith the subject based intent classification and the partial searchphrase; presenting, by said processor to a user via an autocompleteselection mechanism comprising a specialized circuit and the GUI, theautocomplete phrase in combination with additional autocomplete phrasesand a single percentage value of said confidence factor, wherein saidsingle percentage value comprises a single composite value indicating atop intent subject classification value associated with saidautocomplete phrase in combination with additional autocomplete phrases,and wherein said single percentage value of said confidence factor ispresented in a specified arrangement, via said GUI, adjacent to and inbetween said partial search phrase entered in said search field and saidautocomplete phrase in combination with additional autocomplete phrases;executing, by said processor, an improved Web based search with respectto said autocomplete phrase; directing, by said processor based onresults of said executing, a Web application to a specified Web locationassociated with said autocomplete phrase and said Website; presenting,by said processor based on results of said directing, classes ofinformation associated with said autocomplete phrase, wherein saidclasses of information are configured to enable said triggering;receiving, by said processor, additional context associated with anadditional subset of results determined to be more relevant to saiduser; and refining, by said processor executing an auto complete circuitof said embedded hardware device, said additional subset of resultsbased on a specific determined intent of the user thereby yielding atailored list of auto-completion results for said partial search phrase.9. The computer program product of claim 8, wherein the intent baseddata comprises data retrieved during previous search queries associatedwith the subject based intent classification.
 10. The computer programproduct of claim 8, wherein said method further comprises: transmitting,by the processor to the intent data repository, said autocompletephrase, wherein the autocomplete phrase is stored in the intent datarepository.
 11. The computer program product of claim 8, wherein thequery is associated with a specified Website network.
 12. The computerprogram product of claim 8, wherein the presenting the autocompletephrase comprises presenting a confidence percentage value with theautocomplete phrase, and wherein the confidence percentage value isassociated with a confidence factor with respect to the intentclassification.
 13. The computer program product of claim 12, whereinthe method further comprises: presenting, by the processor, additionalsuggested autocomplete phrases associated with the said confidencepercentage value and the autocomplete phrase.
 14. A hardware devicecomprising a processor coupled to a computer-readable memory unit, thememory unit comprising instructions that when executed by the processorexecutes a Website search query improvement method comprising:analyzing, by said processor enabling a natural language classifier(NLC) circuit of an embedded hardware device, a partial search phraseentered in a search field of a graphical user interface (GUI) withrespect to a search query for specified subject matter; continuouslydetecting, by the NLC circuit via execution of an automated naturallanguage search query, all possible patterns of the partial searchphrase; matching, by the NLC circuit, all of the detected possiblepatterns with respect to information within an autocomplete resultrepository database; executing, by the NLC circuit, a deep learningprocess for automated prediction of classes and categories for thepartial search phrase; triggering, by the NLC circuit, an action withrespect to software application execution; determining via applicationof an application programming interface (API), by the processorexecuting the NLC circuit with respect to results of the analyzing, asubject based intent classification associated with the search query forcontent classifications defined within a ground truth for the Website;determining, by the NLC circuit, that said subject based intentclassification comprises a confidence factor of less than 100 percentconfidence with respect to the subject based intent classification beingcorrect; comparing, by the processor, the subject based intentclassification to intent based data of an intent data repository;generating, by said processor based on results of said determining thatsaid subject based intent classification comprises a confidence factorof less than 100 percent confidence and said comparing, a subset ofsearch results of search results data, wherein said subset of searchresults correlates to said subject based intent classification;automatically generating, by the processor based on results of thecomparing, the matching, the executing, the triggering, the generating,and the detecting, an autocomplete phrase associated with the subjectbased intent classification and the partial search phrase; presenting,by said processor to a user via an autocomplete selection mechanismcomprising a specialized circuit and the GUI, the autocomplete phrase incombination with additional autocomplete phrases and a single percentagevalue of said confidence factor, wherein said single percentage valuecomprises a single composite value indicating a top intent subjectclassification value associated with said autocomplete phrase incombination with additional autocomplete phrases, and wherein saidsingle percentage value of said confidence factor is presented in aspecified arrangement, via said GUI, adjacent to and in between saidpartial search phrase entered in said search field and said autocompletephrase in combination with additional autocomplete phrases; executing,by said processor, an improved Web based search with respect to saidautocomplete phrase; directing, by said processor based on results ofsaid executing, a Web application to a specified Web location associatedwith said autocomplete phrase and said Website; presenting, by saidprocessor based on results of said directing, classes of informationassociated with said autocomplete phrase, wherein said classes ofinformation are configured to enable said triggering; receiving, by saidprocessor, additional context associated with an additional subset ofresults determined to be more relevant to said user; and refining, bysaid processor executing an auto complete circuit of said embeddedhardware device, said additional subset of results based on a specificdetermined intent of the user thereby yielding a tailored list ofauto-completion results for said partial search phrase.
 15. The hardwaredevice of claim 14, wherein the intent based data comprises dataretrieved during previous search queries associated with the subjectbased intent classification.
 16. The hardware device of claim 14,wherein said method further comprises: transmitting, by the processor tothe intent data repository, said autocomplete phrase, wherein theautocomplete phrase is stored in the intent data repository.
 17. Thehardware device of claim 14, wherein the query is associated with aspecified Website network.